One-Step Event-Driven High-Speed Autofocus

• URL: http://arxiv.org/abs/2503.01214v1
• Date: Mon, 03 Mar 2025 06:25:09 GMT
• Title: One-Step Event-Driven High-Speed Autofocus
• Authors: Yuhan Bao, Shaohua Gao, Wenyong Li, Kaiwei Wang,
• Abstract summary: Event Laplacian Product (ELP) focus detection function combines event data with grayscale Laplacian information, redefining focus search as a detection task.<n>This innovation enables the first one-step event-driven autofocus, cutting focusing time by up to two-thirds and reducing focusing error by 24 times on the DAVIS346 dataset and 22 times on the EVK4 dataset.
• Score: 2.5565719921628443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-speed autofocus in extreme scenes remains a significant challenge. Traditional methods rely on repeated sampling around the focus position, resulting in ``focus hunting''. Event-driven methods have advanced focusing speed and improved performance in low-light conditions; however, current approaches still require at least one lengthy round of ``focus hunting'', involving the collection of a complete focus stack. We introduce the Event Laplacian Product (ELP) focus detection function, which combines event data with grayscale Laplacian information, redefining focus search as a detection task. This innovation enables the first one-step event-driven autofocus, cutting focusing time by up to two-thirds and reducing focusing error by 24 times on the DAVIS346 dataset and 22 times on the EVK4 dataset. Additionally, we present an autofocus pipeline tailored for event-only cameras, achieving accurate results across a range of challenging motion and lighting conditions. All datasets and code will be made publicly available.

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